CN115713399B - User credit evaluation system combined with third-party data source - Google Patents
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Abstract
The invention discloses a user credit evaluation system combined with a third party data source, which comprises: the acquisition module is used for acquiring a third party data source; the data processing module is used for carrying out data processing on the third-party data source to obtain target data; and the evaluation module is used for carrying out combined label processing on the target data to form a code value label, and evaluating the credit of the user according to the code value label to obtain an evaluation result. The high-quality third party data sources are screened and standardized output is formed, the third party data sources can be conveniently and quickly docked by a mechanism, the docking efficiency can be ensured, the data safety in the execution process is ensured, and meanwhile, the accuracy of the obtained evaluation result is improved.
Description
Technical Field
The invention relates to the technical field of credit evaluation, in particular to a user credit evaluation system combined with a third-party data source.
Background
At present, in the present day of the vigorous development of the financial credit industry, the problem of information asymmetry exists in financial institutions in the Chinese market, data sharing cannot be rapidly and accurately realized, third party data cannot be accurately screened, and the problem of inaccurate credit assessment of users is caused.
Disclosure of Invention
The present invention aims to solve, at least to some extent, one of the technical problems in the above-described technology. Therefore, the invention aims to provide a user credit evaluation system combined with a third party data source, which screens high-quality third party data sources and forms standardized output, can conveniently and quickly dock the third party data sources by a mechanism, can ensure the docking efficiency and the data safety in the executing process, and also improves the accuracy of the obtained evaluation result.
To achieve the above object, an embodiment of the present invention provides a user credit evaluation system combined with a third party data source, including:
the acquisition module is used for acquiring a third party data source;
the data processing module is used for carrying out data processing on the third-party data source to obtain target data;
and the evaluation module is used for carrying out combined label processing on the target data to form a code value label, and evaluating the credit of the user according to the code value label to obtain an evaluation result.
According to some embodiments of the invention, the data processing module comprises:
the screening module is used for carrying out data screening on the third-party data source based on a preset rule to obtain screening data;
and the processing module is used for processing the derivative variables of the screening data to obtain target data.
According to some embodiments of the invention, the preset rules include blacklist class, multi-head class, scoring class, early warning class and verification class; wherein, the liquid crystal display device comprises a liquid crystal display device,
the blacklist class comprises a blacklist type, a high-risk list and a gray list;
the multi-head class comprises D90_ID card number_total application institution number, D180_ID card number_total application institution number, last 6 months_credit times and last 24 months_credit times;
the scoring includes credit scoring and fraud scoring;
the early warning class comprises an early warning grade;
the authentication class includes a duration on the web and a state on the web.
According to some embodiments of the invention, the manner in which the derivative variable is processed includes: calculating, logically judging processing, counting and weight-removing counting and other processing indexes; wherein, the liquid crystal display device comprises a liquid crystal display device,
the calculation includes whether the blacklist is severely overdue and whether the user number is empty;
the logic judging processing comprises information merging under the same user identity card and mobile phone number, and outputting variables after the merging logic processing; logic processing includes determining at least one of a maximum value, a minimum value, or a sum;
the counting and repeated counting with the row number comprises repeated login times of the same account, customer data volume of the same residence address application credit of the user and telephone number of different working units with the same working unit name;
the other processing indexes include:
calculating time difference, including the time difference between the last application and the present;
analyzing longitude and latitude, namely analyzing province and city according to longitude and latitude, and calculating a direct distance according to two groups of longitude and latitude data;
applying classification counting, including counting the number of various APP installed by a user according to APP classification labels given by risks;
inquiring the identity of the user, including inquiring whether the user is a client according to the mobile phone number;
other custom logic includes whether to apply for nighttime or whether to apply for non-silver institutions.
According to some embodiments of the invention, the data processing module further comprises:
the desensitization module is used for detecting the third party data source before the screening module performs data screening on the third party data source based on a preset rule, judging whether sensitive data exist or not, and performing desensitization processing when the sensitive data exist.
According to some embodiments of the invention, the acquisition module comprises: each data source interface is used for receiving different types of third party data sources.
According to some embodiments of the invention, the method further comprises a storage module for storing the evaluation result.
According to some embodiments of the invention, the desensitizing module comprises:
the conversion module is used for converting the third-party data source into a character string;
and the matching module is used for matching the character string with the sensitive character string in the sensitive database and judging whether sensitive data exists according to a matching result.
According to some embodiments of the invention, the evaluation module comprises:
a fusion module for:
classifying the code value labels according to different scenes, determining code value labels corresponding to the multiple scenes respectively by a user, and establishing a binding relationship between each scene and the corresponding code value label as an evaluation vector;
determining a feature space of the corresponding scene category according to the evaluation vector;
mapping the feature space corresponding to each scene category to obtain a plurality of kernel spaces, wherein the kernel spaces comprise association relations between evaluation vectors;
normalizing the plurality of kernel spaces to obtain a plurality of target kernel spaces;
acquiring a weight coefficient corresponding to each scene category in a plurality of scene categories;
fusing according to the target kernel spaces and the weight coefficients to obtain a fused kernel space;
the establishing module is used for:
acquiring credit data corresponding to each sample code value label in a sample code value label set;
screening the sample code value label set to determine a target sample code value label set;
determining a corresponding sample fusion kernel space based on sample code value labels in the target sample code value label set;
analyzing the credit data to determine a credit score;
establishing a matching relation between the credit score and the sample fusion nuclear space, and generating a database of the credit score and the sample fusion nuclear space;
establishing a sample fusion kernel space protocol dictionary in different dimensions for a sample fusion kernel space in the database;
establishing a regression model of credit scores matched with the sample fusion kernel space protocol dictionary and the sample fusion kernel space based on a regression algorithm;
and the determining module is used for carrying out classification recognition and compensation processing on the fusion nuclear space according to the regression model and determining an evaluation result.
According to some embodiments of the invention, the establishing module comprises:
the numerical processing module is used for performing numerical processing on a plurality of sample code value labels included in the sample code value label set to obtain a data matrix; each sample code value label comprises corresponding numerical values of all parameters in the user credit evaluation parameters and corresponding user data of the corresponding user credit evaluation parameters;
the rejecting module is used for:
calculating the data duty ratio of each parameter in the credit evaluation parameters of the user according to the data matrix and a first preset algorithm;
calculating the credit data value label of the user corresponding to each sample code value label according to the data duty ratio and a second preset algorithm, comparing the credit data value label with a first preset threshold value and a second preset threshold value respectively, removing the sample code value label corresponding to the credit data value label larger than the first preset threshold value and the sample code value label corresponding to the credit data value label smaller than the second preset threshold value, and obtaining a target sample code value label set; the first preset threshold is greater than the second preset threshold.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a block diagram of a user credit assessment system incorporating a third party data source in accordance with one embodiment of the invention;
FIG. 2 is a block diagram of a data processing module according to one embodiment of the invention;
FIG. 3 is a block diagram of an assessment module according to one embodiment of the invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
As shown in fig. 1, an embodiment of the present invention proposes a user credit evaluation system combined with a third party data source, including:
the acquisition module is used for acquiring a third party data source;
the data processing module is used for carrying out data processing on the third-party data source to obtain target data;
and the evaluation module is used for carrying out combined label processing on the target data to form a code value label, and evaluating the credit of the user according to the code value label to obtain an evaluation result.
The working principle of the technical scheme is as follows: the code value tags represent tag data for the user in each scene. The acquisition module is used for acquiring a third party data source; the data processing module is used for carrying out data processing on the third-party data source to obtain target data; and the evaluation module is used for carrying out combined label processing on the target data to form a code value label, and evaluating the credit of the user according to the code value label to obtain an evaluation result.
The beneficial effects of the technical scheme are that: the high-quality third party data sources are screened and standardized output is formed, the third party data sources can be conveniently and quickly docked by a mechanism, the docking efficiency can be ensured, the data safety in the execution process is ensured, and meanwhile, the accuracy of the obtained evaluation result is improved.
As shown in fig. 2, according to some embodiments of the invention, the data processing module includes:
the screening module is used for carrying out data screening on the third-party data source based on a preset rule to obtain screening data;
and the processing module is used for processing the derivative variables of the screening data to obtain target data.
The working principle of the technical scheme is as follows: the screening module is used for carrying out data screening on the third-party data source based on a preset rule to obtain screening data; and the processing module is used for processing the derivative variables of the screening data to obtain target data.
The beneficial effects of the technical scheme are that: and the third party data source is subjected to data screening based on a preset rule, so that the data is convenient to be normalized integrally, various data are extracted, the various data are further convenient to be subjected to derivative variable processing, target data are obtained, the data processing efficiency is convenient to be improved, the target data are convenient to be obtained rapidly, and high-quality data are screened.
According to some embodiments of the invention, the preset rules include blacklist class, multi-head class, scoring class, early warning class and verification class; wherein, the liquid crystal display device comprises a liquid crystal display device,
the blacklist class comprises a blacklist type, a high-risk list and a gray list;
the multi-head class comprises D90_ID card number_total application institution number, D180_ID card number_total application institution number, last 6 months_credit times and last 24 months_credit times;
the scoring includes credit scoring and fraud scoring;
the early warning class comprises an early warning grade;
the authentication class includes a duration on the web and a state on the web.
The working principle of the technical scheme is as follows: d90_id number_total number of application institutions and d180_id number_total number of application institutions represent relevant information of users in different areas.
The beneficial effects of the technical scheme are that: and the effective screening and classification of the data are realized.
According to some embodiments of the invention, the manner in which the derivative variable is processed includes: calculating, logically judging processing, counting and weight-removing counting and other processing indexes; wherein, the liquid crystal display device comprises a liquid crystal display device,
the calculation includes whether the blacklist is severely overdue and whether the user number is empty;
the logic judging processing comprises information merging under the same user identity card and mobile phone number, and outputting variables after the merging logic processing; logic processing includes determining at least one of a maximum value, a minimum value, or a sum;
the counting and repeated counting with the row number comprises repeated login times of the same account, customer data volume of the same residence address application credit of the user and telephone number of different working units with the same working unit name;
the other processing indexes include:
calculating time difference, including the time difference between the last application and the present;
analyzing longitude and latitude, namely analyzing province and city according to longitude and latitude, and calculating a direct distance according to two groups of longitude and latitude data;
applying classification counting, including counting the number of various APP installed by a user according to APP classification labels given by risks;
inquiring the identity of the user, including inquiring whether the user is a client according to the mobile phone number;
other custom logic includes whether to apply for nighttime or whether to apply for non-silver institutions.
The technical scheme has the working principle and beneficial effects that: based on the collected basic data, processing derivative variables in real time, and particularly relates to the following processing modes:
(1) Comprises the following steps: (EXIST)
For example: whether the blacklist is severely overdue, whether the subscriber number is a null number, etc
(2) Logic judgment processing:
for example: information under the same user identity card and mobile phone number is merged, and variables are output after merging logic processing (maximum, minimum, addition and the like).
(3) Counting, including weight-removing counting: (COUNT, COUNT DISTINCT)
For example: repeated login times of the same account, customer data volume of the same residence address application trust of the user, telephone number of different working units with the same working unit name
(4) Other processing indexes:
calculation of time differences, e.g. of the last application to date
Longitude and latitude analysis, e.g. calculating direct distance from two sets of longitude and latitude data
-applying a class count, e.g. counting the number of classes of APP installed by a user according to a risk given APP class label
( And (3) injection: APP class list should support subsequent manual addition, deletion, and modification )
User identity inquiry, e.g. whether or not it is a client based on a cell phone number
Other customization logic, such as whether to apply for night, whether to apply for non-silver institution, etc
The historical data needs to be stored for two years, and indexes of an undefined inquiry time window are counted based on the full effective historical data; derived variables relating to a particular time window, such as the time window is counted on a natural day for more than 5 days and on a minute for less than 5 days. Different data processing is conveniently carried out on different data, and target data can be conveniently and accurately obtained.
According to some embodiments of the invention, the data processing module further comprises:
the desensitization module is used for detecting the third party data source before the screening module performs data screening on the third party data source based on a preset rule, judging whether sensitive data exist or not, and performing desensitization processing when the sensitive data exist.
The beneficial effects of the technical scheme are that: and the data security is convenient to improve.
According to some embodiments of the invention, the acquisition module comprises: each data source interface is used for receiving different types of third party data sources.
According to some embodiments of the invention, the method further comprises a storage module for storing the evaluation result.
According to some embodiments of the invention, the desensitizing module comprises:
the conversion module is used for converting the third-party data source into a character string;
and the matching module is used for matching the character string with the sensitive character string in the sensitive database and judging whether sensitive data exists according to a matching result.
The beneficial effects of the technical scheme are that: and based on the matching result of the character string and the sensitive character string, accurately judging whether sensitive data exists or not. When the matching degree is larger than the preset matching degree, sensitive data are indicated; otherwise, no is indicated.
As shown in fig. 3, according to some embodiments of the invention, the evaluation module includes:
a fusion module for:
classifying the code value labels according to different scenes, determining code value labels corresponding to the multiple scenes respectively by a user, and establishing a binding relationship between each scene and the corresponding code value label as an evaluation vector;
determining a feature space of the corresponding scene category according to the evaluation vector;
mapping the feature space corresponding to each scene category to obtain a plurality of kernel spaces, wherein the kernel spaces comprise association relations between evaluation vectors;
normalizing the plurality of kernel spaces to obtain a plurality of target kernel spaces;
acquiring a weight coefficient corresponding to each scene category in a plurality of scene categories;
fusing according to the target kernel spaces and the weight coefficients to obtain a fused kernel space;
the establishing module is used for:
acquiring credit data corresponding to each sample code value label in a sample code value label set;
screening the sample code value label set to determine a target sample code value label set;
determining a corresponding sample fusion kernel space based on sample code value labels in the target sample code value label set;
analyzing the credit data to determine a credit score;
establishing a matching relation between the credit score and the sample fusion nuclear space, and generating a database of the credit score and the sample fusion nuclear space;
establishing a sample fusion kernel space protocol dictionary in different dimensions for a sample fusion kernel space in the database;
establishing a regression model of credit scores matched with the sample fusion kernel space protocol dictionary and the sample fusion kernel space based on a regression algorithm;
and the determining module is used for carrying out classification recognition and compensation processing on the fusion nuclear space according to the regression model and determining an evaluation result.
The technical scheme has the working principle and beneficial effects that: a fusion module for: classifying the code value labels according to different scenes, determining code value labels corresponding to the multiple scenes respectively by a user, and establishing a binding relationship between each scene and the corresponding code value label as an evaluation vector; determining a feature space of the corresponding scene category according to the evaluation vector; mapping the feature space corresponding to each scene category to obtain a plurality of kernel spaces, wherein the kernel spaces comprise association relations between evaluation vectors; normalizing the plurality of kernel spaces to obtain a plurality of target kernel spaces; acquiring a weight coefficient corresponding to each scene category in a plurality of scene categories; fusing according to the target kernel spaces and the weight coefficients to obtain a fused kernel space; the code value labels of different scenes of the user are conveniently displayed, the overall evaluation space of the user, namely the fusion kernel space, is determined, and the comprehensive data of the user are represented. The establishing module is used for: acquiring credit data corresponding to each sample code value label in a sample code value label set; screening the sample code value label set to determine a target sample code value label set; determining a corresponding sample fusion kernel space based on sample code value labels in the target sample code value label set; analyzing the credit data to determine a credit score; establishing a matching relation between the credit score and the sample fusion nuclear space, and generating a database of the credit score and the sample fusion nuclear space; establishing a sample fusion kernel space protocol dictionary in different dimensions for a sample fusion kernel space in the database; establishing a regression model of credit scores matched with the sample fusion kernel space protocol dictionary and the sample fusion kernel space based on a regression algorithm; and the determining module is used for carrying out classification recognition and compensation processing on the fusion nuclear space according to the regression model and determining an evaluation result. And establishing a regression model based on the sample code value label set and credit data corresponding to each sample code value label in the sample code value label set, and carrying out classification recognition and compensation processing on the fusion kernel space based on the regression model to accurately determine an evaluation result.
According to some embodiments of the invention, the establishing module comprises:
the numerical processing module is used for performing numerical processing on a plurality of sample code value labels included in the sample code value label set to obtain a data matrix; each sample code value label comprises corresponding numerical values of all parameters in the user credit evaluation parameters and corresponding user data of the corresponding user credit evaluation parameters;
the rejecting module is used for:
calculating the data duty ratio of each parameter in the credit evaluation parameters of the user according to the data matrix and a first preset algorithm;
calculating the credit data value label of the user corresponding to each sample code value label according to the data duty ratio and a second preset algorithm, comparing the credit data value label with a first preset threshold value and a second preset threshold value respectively, removing the sample code value label corresponding to the credit data value label larger than the first preset threshold value and the sample code value label corresponding to the credit data value label smaller than the second preset threshold value, and obtaining a target sample code value label set; the first preset threshold is greater than the second preset threshold.
The working principle of the technical scheme is as follows: the numerical processing module is used for performing numerical processing on a plurality of sample code value labels included in the sample code value label set to obtain a data matrix; each sample code value label comprises corresponding numerical values of all parameters in the user credit evaluation parameters and corresponding user data of the corresponding user credit evaluation parameters; the rejecting module is used for: calculating the data duty ratio of each parameter in the credit evaluation parameters of the user according to the data matrix and a first preset algorithm; calculating the credit data value label of the user corresponding to each sample code value label according to the data duty ratio and a second preset algorithm, comparing the credit data value label with a first preset threshold value and a second preset threshold value respectively, removing the sample code value label corresponding to the credit data value label larger than the first preset threshold value and the sample code value label corresponding to the credit data value label smaller than the second preset threshold value, and obtaining a target sample code value label set; the first preset threshold is greater than the second preset threshold.
The beneficial effects of the technical scheme are that: the data are preprocessed, so that the value difference among parameters is not overlarge, the extreme value of the credit data value of the user corresponding to the sample code value label is removed, the accuracy of data screening is improved, and the accuracy of the obtained target sample code value label set is ensured.
In one embodiment, calculating the data duty ratio of each of the credit evaluation parameters of the user according to the data matrix and the first preset algorithm includes:
wherein w is j The data duty ratio of the j-th parameter in the credit evaluation parameters for the user; p is the number of sample code value tags included in the sample code value tag set; x is X i,j A value obtained after data preprocessing is carried out on the j-th parameter in the user credit evaluation parameters of the i-th sample code value label in the P-th sample code value labels;
based on the formula, the data duty ratio of each parameter in the credit evaluation parameters of the user is accurately calculated.
According to the data duty ratio and a second preset algorithm, calculating a user credit data score corresponding to each sample code value label, including:
F i ＝w 1 *X i,1 +w 2 *X i,2 +w 3 *X i,3 +w 4 *X i,4
wherein F is i And the user credit data score corresponding to the ith sample code value label in the P sample code value labels.
Based on the formula, the credit data score of the user corresponding to each sample code value label is accurately calculated, the accuracy of judging the sizes of the first preset threshold value and the second preset threshold value is improved, and further the data to be removed is accurately determined.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (8)
1. A user credit assessment system in combination with a third party data source, comprising:
the acquisition module is used for acquiring a third party data source;
the data processing module is used for carrying out data processing on the third-party data source to obtain target data;
the evaluation module is used for carrying out combined label processing on the target data to form a code value label, and evaluating the credit of the user according to the code value label to obtain an evaluation result;
the evaluation module comprises:
a fusion module for:
classifying the code value labels according to different scenes, determining code value labels corresponding to the multiple scenes respectively by a user, and establishing a binding relationship between each scene and the corresponding code value label as an evaluation vector;
determining a feature space of the corresponding scene category according to the evaluation vector;
mapping the feature space corresponding to each scene category to obtain a plurality of kernel spaces, wherein the kernel spaces comprise association relations between evaluation vectors;
normalizing the plurality of kernel spaces to obtain a plurality of target kernel spaces;
acquiring a weight coefficient corresponding to each scene category in a plurality of scene categories;
fusing according to the target kernel spaces and the weight coefficients to obtain a fused kernel space;
the establishing module is used for:
acquiring credit data corresponding to each sample code value label in a sample code value label set;
screening the sample code value label set to determine a target sample code value label set;
determining a corresponding sample fusion kernel space based on sample code value labels in the target sample code value label set;
analyzing the credit data to determine a credit score;
establishing a matching relation between the credit score and the sample fusion nuclear space, and generating a database of the credit score and the sample fusion nuclear space;
establishing a sample fusion kernel space protocol dictionary in different dimensions for a sample fusion kernel space in the database;
establishing a regression model of credit scores matched with the sample fusion kernel space protocol dictionary and the sample fusion kernel space based on a regression algorithm;
the determining module is used for carrying out classification recognition and compensation processing on the fusion nuclear space according to the regression model and determining an evaluation result;
the establishing module comprises:
the numerical processing module is used for performing numerical processing on a plurality of sample code value labels included in the sample code value label set to obtain a data matrix; each sample code value label comprises corresponding numerical values of all parameters in the user credit evaluation parameters and corresponding user data of the corresponding user credit evaluation parameters;
the rejecting module is used for:
calculating the data duty ratio of each parameter in the credit evaluation parameters of the user according to the data matrix and a first preset algorithm;
calculating the credit data value label of the user corresponding to each sample code value label according to the data duty ratio and a second preset algorithm, comparing the credit data value label with a first preset threshold value and a second preset threshold value respectively, removing the sample code value label corresponding to the credit data value label larger than the first preset threshold value and the sample code value label corresponding to the credit data value label smaller than the second preset threshold value, and obtaining a target sample code value label set; the first preset threshold is greater than the second preset threshold.
2. The user credit assessment system in combination with a third party data source of claim 1, wherein the data processing module comprises:
the screening module is used for carrying out data screening on the third-party data source based on a preset rule to obtain screening data;
and the processing module is used for processing the derivative variables of the screening data to obtain target data.
3. The user credit assessment system in combination with a third party data source of claim 2, wherein the preset rules include blacklist class, multi-head class, scoring class, early warning class and verification class; wherein, the liquid crystal display device comprises a liquid crystal display device,
the blacklist class comprises a blacklist type, a high-risk list and a gray list;
the multi-head class comprises D90_ID card number_total application institution number, D180_ID card number_total application institution number, last 6 months_credit times and last 24 months_credit times;
the scoring includes credit scoring and fraud scoring;
the early warning class comprises an early warning grade;
the authentication class includes a duration on the web and a state on the web.
4. The user credit assessment system in combination with a third party data source of claim 2, wherein the manner in which the derived variables are processed comprises: calculating, logically judging processing, counting and weight-removing counting and other processing indexes; wherein, the liquid crystal display device comprises a liquid crystal display device,
the calculation includes whether the blacklist is severely overdue and whether the user number is empty;
the logic judging processing comprises information merging under the same user identity card and mobile phone number, and outputting variables after the merging logic processing; logic processing includes determining at least one of a maximum value, a minimum value, or a sum;
the counting and repeated counting with the row number comprises repeated login times of the same account, customer data volume of the same residence address application credit of the user and telephone number of different working units with the same working unit name;
the other processing indexes include:
calculating time difference, including the time difference between the last application and the present;
analyzing longitude and latitude, namely analyzing province and city according to longitude and latitude, and calculating a direct distance according to two groups of longitude and latitude data;
applying classification counting, including counting the number of various APP installed by a user according to APP classification labels given by risks;
inquiring the identity of the user, including inquiring whether the user is a client according to the mobile phone number;
other custom logic includes whether to apply for nighttime or whether to apply for non-silver institutions.
5. The user credit assessment system in combination with a third party data source of claim 1, wherein the data processing module further comprises:
the desensitization module is used for detecting the third party data source before the screening module performs data screening on the third party data source based on a preset rule, judging whether sensitive data exist or not, and performing desensitization processing when the sensitive data exist.
6. The user credit assessment system in combination with a third party data source of claim 1, wherein the acquisition module comprises: each data source interface is used for receiving different types of third party data sources.
7. The user credit assessment system in combination with a third party data source of claim 1, further comprising a storage module for storing the assessment results.
8. The user credit assessment system in combination with a third party data source of claim 5, wherein the desensitization module comprises:
the conversion module is used for converting the third-party data source into a character string;
and the matching module is used for matching the character string with the sensitive character string in the sensitive database and judging whether sensitive data exists according to a matching result.
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